August 2023
library(magrittr) x <- 9 # Calculate the square-root of x sqrt(x)
## [1] 3
# Calculate it using pipes x %>% sqrt
## [1] 3
x <- 9 # Calculate the square-root of x and update x x <- sqrt(x) x
## [1] 3
# Calculate it using pipes x <- 9 x %<>% sqrt x
## [1] 3
df <- read.csv("beedata.csv")
nrow(subset(df, hive==4))
## [1] 60193
df %>% subset(hive==4) %>% nrow
## [1] 60193
# Exercises - Piping # 1. Rewrite the following code using %>% and %<>%: x <- 2 round(log(x))
## [1] 1
# 2. Rewrite the second line of following code: x <- rnorm(10,100) round(sum(sqrt(x)), 3)
## [1] 100.027
x <- 2 round(log(x))
## [1] 1
x <- 2 x %>% log %>% round
## [1] 1
# 2. Rewrite the second line of following code: x <- rnorm(10,100) round(sum(sqrt(x)), 3)
## [1] 99.993
x %>% sqrt %>% sum %>% round(3)
## [1] 99.993
Artwork by @allison_horst
Horst AM, Hill AP, Gorman KB (2020). palmerpenguins: Palmer Archipelago (Antarctica) penguin data. R package version 0.1.0. https://allisonhorst.github.io/palmerpenguins/. doi: 10.5281/zenodo.3960218.
https://allisonhorst.github.io/palmerpenguins/
library(palmerpenguins)
## ## Attaching package: 'palmerpenguins'
## The following objects are masked from 'package:datasets': ## ## penguins, penguins_raw
head(penguins)
## # A tibble: 6 × 8 ## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g ## <fct> <fct> <dbl> <dbl> <int> <int> ## 1 Adelie Torgersen 39.1 18.7 181 3750 ## 2 Adelie Torgersen 39.5 17.4 186 3800 ## 3 Adelie Torgersen 40.3 18 195 3250 ## 4 Adelie Torgersen NA NA NA NA ## 5 Adelie Torgersen 36.7 19.3 193 3450 ## 6 Adelie Torgersen 39.3 20.6 190 3650 ## # ℹ 2 more variables: sex <fct>, year <int>
https://github.com/mcnakhaee/palmerpenguins?tab=readme-ov-file
from palmerpenguins import load_penguins
## /home/diseng001/R/x86_64-pc-linux-gnu-library/4.5/reticulate/python/rpytools/loader.py:120: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. ## return _find_and_load(name, import_)
penguins = load_penguins() penguins.head()
## species island bill_length_mm ... body_mass_g sex year ## 0 Adelie Torgersen 39.1 ... 3750.0 male 2007 ## 1 Adelie Torgersen 39.5 ... 3800.0 female 2007 ## 2 Adelie Torgersen 40.3 ... 3250.0 female 2007 ## 3 Adelie Torgersen NaN ... NaN NaN 2007 ## 4 Adelie Torgersen 36.7 ... 3450.0 female 2007 ## ## [5 rows x 8 columns]
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year |
|---|---|---|---|---|---|---|---|
| Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | male | 2007 |
| Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | female | 2007 |
| Adelie | Torgersen | 40.3 | 18.0 | 195 | 3250 | female | 2007 |
| Adelie | Torgersen | NA | NA | NA | NA | NA | 2007 |
| Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | female | 2007 |
| Adelie | Torgersen | 39.3 | 20.6 | 190 | 3650 | male | 2007 |
| Adelie | Torgersen | 38.9 | 17.8 | 181 | 3625 | female | 2007 |
| Adelie | Torgersen | 39.2 | 19.6 | 195 | 4675 | male | 2007 |
| Adelie | Torgersen | 34.1 | 18.1 | 193 | 3475 | NA | 2007 |
| Adelie | Torgersen | 42.0 | 20.2 | 190 | 4250 | NA | 2007 |
| Adelie | Torgersen | 37.8 | 17.1 | 186 | 3300 | NA | 2007 |
| Adelie | Torgersen | 37.8 | 17.3 | 180 | 3700 | NA | 2007 |
| Adelie | Torgersen | 41.1 | 17.6 | 182 | 3200 | female | 2007 |
| Adelie | Torgersen | 38.6 | 21.2 | 191 | 3800 | male | 2007 |
| Adelie | Torgersen | 34.6 | 21.1 | 198 | 4400 | male | 2007 |
| Adelie | Torgersen | 36.6 | 17.8 | 185 | 3700 | female | 2007 |
| Adelie | Torgersen | 38.7 | 19.0 | 195 | 3450 | female | 2007 |
| Adelie | Torgersen | 42.5 | 20.7 | 197 | 4500 | male | 2007 |
| Adelie | Torgersen | 34.4 | 18.4 | 184 | 3325 | female | 2007 |
| Adelie | Torgersen | 46.0 | 21.5 | 194 | 4200 | male | 2007 |
| Adelie | Biscoe | 37.8 | 18.3 | 174 | 3400 | female | 2007 |
| Adelie | Biscoe | 37.7 | 18.7 | 180 | 3600 | male | 2007 |
| Adelie | Biscoe | 35.9 | 19.2 | 189 | 3800 | female | 2007 |
| Adelie | Biscoe | 38.2 | 18.1 | 185 | 3950 | male | 2007 |
| Adelie | Biscoe | 38.8 | 17.2 | 180 | 3800 | male | 2007 |
| Adelie | Biscoe | 35.3 | 18.9 | 187 | 3800 | female | 2007 |
| Adelie | Biscoe | 40.6 | 18.6 | 183 | 3550 | male | 2007 |
| Adelie | Biscoe | 40.5 | 17.9 | 187 | 3200 | female | 2007 |
| Adelie | Biscoe | 37.9 | 18.6 | 172 | 3150 | female | 2007 |
| Adelie | Biscoe | 40.5 | 18.9 | 180 | 3950 | male | 2007 |
| Adelie | Dream | 39.5 | 16.7 | 178 | 3250 | female | 2007 |
| Adelie | Dream | 37.2 | 18.1 | 178 | 3900 | male | 2007 |
| Adelie | Dream | 39.5 | 17.8 | 188 | 3300 | female | 2007 |
| Adelie | Dream | 40.9 | 18.9 | 184 | 3900 | male | 2007 |
| Adelie | Dream | 36.4 | 17.0 | 195 | 3325 | female | 2007 |
| Adelie | Dream | 39.2 | 21.1 | 196 | 4150 | male | 2007 |
| Adelie | Dream | 38.8 | 20.0 | 190 | 3950 | male | 2007 |
| Adelie | Dream | 42.2 | 18.5 | 180 | 3550 | female | 2007 |
| Adelie | Dream | 37.6 | 19.3 | 181 | 3300 | female | 2007 |
| Adelie | Dream | 39.8 | 19.1 | 184 | 4650 | male | 2007 |
| Adelie | Dream | 36.5 | 18.0 | 182 | 3150 | female | 2007 |
| Adelie | Dream | 40.8 | 18.4 | 195 | 3900 | male | 2007 |
| Adelie | Dream | 36.0 | 18.5 | 186 | 3100 | female | 2007 |
| Adelie | Dream | 44.1 | 19.7 | 196 | 4400 | male | 2007 |
| Adelie | Dream | 37.0 | 16.9 | 185 | 3000 | female | 2007 |
| Adelie | Dream | 39.6 | 18.8 | 190 | 4600 | male | 2007 |
| Adelie | Dream | 41.1 | 19.0 | 182 | 3425 | male | 2007 |
| Adelie | Dream | 37.5 | 18.9 | 179 | 2975 | NA | 2007 |
| Adelie | Dream | 36.0 | 17.9 | 190 | 3450 | female | 2007 |
| Adelie | Dream | 42.3 | 21.2 | 191 | 4150 | male | 2007 |
| Adelie | Biscoe | 39.6 | 17.7 | 186 | 3500 | female | 2008 |
| Adelie | Biscoe | 40.1 | 18.9 | 188 | 4300 | male | 2008 |
| Adelie | Biscoe | 35.0 | 17.9 | 190 | 3450 | female | 2008 |
| Adelie | Biscoe | 42.0 | 19.5 | 200 | 4050 | male | 2008 |
| Adelie | Biscoe | 34.5 | 18.1 | 187 | 2900 | female | 2008 |
| Adelie | Biscoe | 41.4 | 18.6 | 191 | 3700 | male | 2008 |
| Adelie | Biscoe | 39.0 | 17.5 | 186 | 3550 | female | 2008 |
| Adelie | Biscoe | 40.6 | 18.8 | 193 | 3800 | male | 2008 |
| Adelie | Biscoe | 36.5 | 16.6 | 181 | 2850 | female | 2008 |
| Adelie | Biscoe | 37.6 | 19.1 | 194 | 3750 | male | 2008 |
| Adelie | Biscoe | 35.7 | 16.9 | 185 | 3150 | female | 2008 |
| Adelie | Biscoe | 41.3 | 21.1 | 195 | 4400 | male | 2008 |
| Adelie | Biscoe | 37.6 | 17.0 | 185 | 3600 | female | 2008 |
| Adelie | Biscoe | 41.1 | 18.2 | 192 | 4050 | male | 2008 |
| Adelie | Biscoe | 36.4 | 17.1 | 184 | 2850 | female | 2008 |
| Adelie | Biscoe | 41.6 | 18.0 | 192 | 3950 | male | 2008 |
| Adelie | Biscoe | 35.5 | 16.2 | 195 | 3350 | female | 2008 |
| Adelie | Biscoe | 41.1 | 19.1 | 188 | 4100 | male | 2008 |
| Adelie | Torgersen | 35.9 | 16.6 | 190 | 3050 | female | 2008 |
| Adelie | Torgersen | 41.8 | 19.4 | 198 | 4450 | male | 2008 |
| Adelie | Torgersen | 33.5 | 19.0 | 190 | 3600 | female | 2008 |
| Adelie | Torgersen | 39.7 | 18.4 | 190 | 3900 | male | 2008 |
| Adelie | Torgersen | 39.6 | 17.2 | 196 | 3550 | female | 2008 |
| Adelie | Torgersen | 45.8 | 18.9 | 197 | 4150 | male | 2008 |
| Adelie | Torgersen | 35.5 | 17.5 | 190 | 3700 | female | 2008 |
| Adelie | Torgersen | 42.8 | 18.5 | 195 | 4250 | male | 2008 |
| Adelie | Torgersen | 40.9 | 16.8 | 191 | 3700 | female | 2008 |
| Adelie | Torgersen | 37.2 | 19.4 | 184 | 3900 | male | 2008 |
| Adelie | Torgersen | 36.2 | 16.1 | 187 | 3550 | female | 2008 |
| Adelie | Torgersen | 42.1 | 19.1 | 195 | 4000 | male | 2008 |
| Adelie | Torgersen | 34.6 | 17.2 | 189 | 3200 | female | 2008 |
| Adelie | Torgersen | 42.9 | 17.6 | 196 | 4700 | male | 2008 |
| Adelie | Torgersen | 36.7 | 18.8 | 187 | 3800 | female | 2008 |
| Adelie | Torgersen | 35.1 | 19.4 | 193 | 4200 | male | 2008 |
| Adelie | Dream | 37.3 | 17.8 | 191 | 3350 | female | 2008 |
| Adelie | Dream | 41.3 | 20.3 | 194 | 3550 | male | 2008 |
| Adelie | Dream | 36.3 | 19.5 | 190 | 3800 | male | 2008 |
| Adelie | Dream | 36.9 | 18.6 | 189 | 3500 | female | 2008 |
| Adelie | Dream | 38.3 | 19.2 | 189 | 3950 | male | 2008 |
| Adelie | Dream | 38.9 | 18.8 | 190 | 3600 | female | 2008 |
| Adelie | Dream | 35.7 | 18.0 | 202 | 3550 | female | 2008 |
| Adelie | Dream | 41.1 | 18.1 | 205 | 4300 | male | 2008 |
| Adelie | Dream | 34.0 | 17.1 | 185 | 3400 | female | 2008 |
| Adelie | Dream | 39.6 | 18.1 | 186 | 4450 | male | 2008 |
| Adelie | Dream | 36.2 | 17.3 | 187 | 3300 | female | 2008 |
| Adelie | Dream | 40.8 | 18.9 | 208 | 4300 | male | 2008 |
| Adelie | Dream | 38.1 | 18.6 | 190 | 3700 | female | 2008 |
| Adelie | Dream | 40.3 | 18.5 | 196 | 4350 | male | 2008 |
| Adelie | Dream | 33.1 | 16.1 | 178 | 2900 | female | 2008 |
| Adelie | Dream | 43.2 | 18.5 | 192 | 4100 | male | 2008 |
| Adelie | Biscoe | 35.0 | 17.9 | 192 | 3725 | female | 2009 |
| Adelie | Biscoe | 41.0 | 20.0 | 203 | 4725 | male | 2009 |
| Adelie | Biscoe | 37.7 | 16.0 | 183 | 3075 | female | 2009 |
| Adelie | Biscoe | 37.8 | 20.0 | 190 | 4250 | male | 2009 |
| Adelie | Biscoe | 37.9 | 18.6 | 193 | 2925 | female | 2009 |
| Adelie | Biscoe | 39.7 | 18.9 | 184 | 3550 | male | 2009 |
| Adelie | Biscoe | 38.6 | 17.2 | 199 | 3750 | female | 2009 |
| Adelie | Biscoe | 38.2 | 20.0 | 190 | 3900 | male | 2009 |
| Adelie | Biscoe | 38.1 | 17.0 | 181 | 3175 | female | 2009 |
| Adelie | Biscoe | 43.2 | 19.0 | 197 | 4775 | male | 2009 |
| Adelie | Biscoe | 38.1 | 16.5 | 198 | 3825 | female | 2009 |
| Adelie | Biscoe | 45.6 | 20.3 | 191 | 4600 | male | 2009 |
| Adelie | Biscoe | 39.7 | 17.7 | 193 | 3200 | female | 2009 |
| Adelie | Biscoe | 42.2 | 19.5 | 197 | 4275 | male | 2009 |
| Adelie | Biscoe | 39.6 | 20.7 | 191 | 3900 | female | 2009 |
| Adelie | Biscoe | 42.7 | 18.3 | 196 | 4075 | male | 2009 |
| Adelie | Torgersen | 38.6 | 17.0 | 188 | 2900 | female | 2009 |
| Adelie | Torgersen | 37.3 | 20.5 | 199 | 3775 | male | 2009 |
| Adelie | Torgersen | 35.7 | 17.0 | 189 | 3350 | female | 2009 |
| Adelie | Torgersen | 41.1 | 18.6 | 189 | 3325 | male | 2009 |
| Adelie | Torgersen | 36.2 | 17.2 | 187 | 3150 | female | 2009 |
| Adelie | Torgersen | 37.7 | 19.8 | 198 | 3500 | male | 2009 |
| Adelie | Torgersen | 40.2 | 17.0 | 176 | 3450 | female | 2009 |
| Adelie | Torgersen | 41.4 | 18.5 | 202 | 3875 | male | 2009 |
| Adelie | Torgersen | 35.2 | 15.9 | 186 | 3050 | female | 2009 |
| Adelie | Torgersen | 40.6 | 19.0 | 199 | 4000 | male | 2009 |
| Adelie | Torgersen | 38.8 | 17.6 | 191 | 3275 | female | 2009 |
| Adelie | Torgersen | 41.5 | 18.3 | 195 | 4300 | male | 2009 |
| Adelie | Torgersen | 39.0 | 17.1 | 191 | 3050 | female | 2009 |
| Adelie | Torgersen | 44.1 | 18.0 | 210 | 4000 | male | 2009 |
| Adelie | Torgersen | 38.5 | 17.9 | 190 | 3325 | female | 2009 |
| Adelie | Torgersen | 43.1 | 19.2 | 197 | 3500 | male | 2009 |
| Adelie | Dream | 36.8 | 18.5 | 193 | 3500 | female | 2009 |
| Adelie | Dream | 37.5 | 18.5 | 199 | 4475 | male | 2009 |
| Adelie | Dream | 38.1 | 17.6 | 187 | 3425 | female | 2009 |
| Adelie | Dream | 41.1 | 17.5 | 190 | 3900 | male | 2009 |
| Adelie | Dream | 35.6 | 17.5 | 191 | 3175 | female | 2009 |
| Adelie | Dream | 40.2 | 20.1 | 200 | 3975 | male | 2009 |
| Adelie | Dream | 37.0 | 16.5 | 185 | 3400 | female | 2009 |
| Adelie | Dream | 39.7 | 17.9 | 193 | 4250 | male | 2009 |
| Adelie | Dream | 40.2 | 17.1 | 193 | 3400 | female | 2009 |
| Adelie | Dream | 40.6 | 17.2 | 187 | 3475 | male | 2009 |
| Adelie | Dream | 32.1 | 15.5 | 188 | 3050 | female | 2009 |
| Adelie | Dream | 40.7 | 17.0 | 190 | 3725 | male | 2009 |
| Adelie | Dream | 37.3 | 16.8 | 192 | 3000 | female | 2009 |
| Adelie | Dream | 39.0 | 18.7 | 185 | 3650 | male | 2009 |
| Adelie | Dream | 39.2 | 18.6 | 190 | 4250 | male | 2009 |
| Adelie | Dream | 36.6 | 18.4 | 184 | 3475 | female | 2009 |
| Adelie | Dream | 36.0 | 17.8 | 195 | 3450 | female | 2009 |
| Adelie | Dream | 37.8 | 18.1 | 193 | 3750 | male | 2009 |
| Adelie | Dream | 36.0 | 17.1 | 187 | 3700 | female | 2009 |
| Adelie | Dream | 41.5 | 18.5 | 201 | 4000 | male | 2009 |
| Gentoo | Biscoe | 46.1 | 13.2 | 211 | 4500 | female | 2007 |
| Gentoo | Biscoe | 50.0 | 16.3 | 230 | 5700 | male | 2007 |
| Gentoo | Biscoe | 48.7 | 14.1 | 210 | 4450 | female | 2007 |
| Gentoo | Biscoe | 50.0 | 15.2 | 218 | 5700 | male | 2007 |
| Gentoo | Biscoe | 47.6 | 14.5 | 215 | 5400 | male | 2007 |
| Gentoo | Biscoe | 46.5 | 13.5 | 210 | 4550 | female | 2007 |
| Gentoo | Biscoe | 45.4 | 14.6 | 211 | 4800 | female | 2007 |
| Gentoo | Biscoe | 46.7 | 15.3 | 219 | 5200 | male | 2007 |
| Gentoo | Biscoe | 43.3 | 13.4 | 209 | 4400 | female | 2007 |
| Gentoo | Biscoe | 46.8 | 15.4 | 215 | 5150 | male | 2007 |
| Gentoo | Biscoe | 40.9 | 13.7 | 214 | 4650 | female | 2007 |
| Gentoo | Biscoe | 49.0 | 16.1 | 216 | 5550 | male | 2007 |
| Gentoo | Biscoe | 45.5 | 13.7 | 214 | 4650 | female | 2007 |
| Gentoo | Biscoe | 48.4 | 14.6 | 213 | 5850 | male | 2007 |
| Gentoo | Biscoe | 45.8 | 14.6 | 210 | 4200 | female | 2007 |
| Gentoo | Biscoe | 49.3 | 15.7 | 217 | 5850 | male | 2007 |
| Gentoo | Biscoe | 42.0 | 13.5 | 210 | 4150 | female | 2007 |
| Gentoo | Biscoe | 49.2 | 15.2 | 221 | 6300 | male | 2007 |
| Gentoo | Biscoe | 46.2 | 14.5 | 209 | 4800 | female | 2007 |
| Gentoo | Biscoe | 48.7 | 15.1 | 222 | 5350 | male | 2007 |
| Gentoo | Biscoe | 50.2 | 14.3 | 218 | 5700 | male | 2007 |
| Gentoo | Biscoe | 45.1 | 14.5 | 215 | 5000 | female | 2007 |
| Gentoo | Biscoe | 46.5 | 14.5 | 213 | 4400 | female | 2007 |
| Gentoo | Biscoe | 46.3 | 15.8 | 215 | 5050 | male | 2007 |
| Gentoo | Biscoe | 42.9 | 13.1 | 215 | 5000 | female | 2007 |
| Gentoo | Biscoe | 46.1 | 15.1 | 215 | 5100 | male | 2007 |
| Gentoo | Biscoe | 44.5 | 14.3 | 216 | 4100 | NA | 2007 |
| Gentoo | Biscoe | 47.8 | 15.0 | 215 | 5650 | male | 2007 |
| Gentoo | Biscoe | 48.2 | 14.3 | 210 | 4600 | female | 2007 |
| Gentoo | Biscoe | 50.0 | 15.3 | 220 | 5550 | male | 2007 |
| Gentoo | Biscoe | 47.3 | 15.3 | 222 | 5250 | male | 2007 |
| Gentoo | Biscoe | 42.8 | 14.2 | 209 | 4700 | female | 2007 |
| Gentoo | Biscoe | 45.1 | 14.5 | 207 | 5050 | female | 2007 |
| Gentoo | Biscoe | 59.6 | 17.0 | 230 | 6050 | male | 2007 |
| Gentoo | Biscoe | 49.1 | 14.8 | 220 | 5150 | female | 2008 |
| Gentoo | Biscoe | 48.4 | 16.3 | 220 | 5400 | male | 2008 |
| Gentoo | Biscoe | 42.6 | 13.7 | 213 | 4950 | female | 2008 |
| Gentoo | Biscoe | 44.4 | 17.3 | 219 | 5250 | male | 2008 |
| Gentoo | Biscoe | 44.0 | 13.6 | 208 | 4350 | female | 2008 |
| Gentoo | Biscoe | 48.7 | 15.7 | 208 | 5350 | male | 2008 |
| Gentoo | Biscoe | 42.7 | 13.7 | 208 | 3950 | female | 2008 |
| Gentoo | Biscoe | 49.6 | 16.0 | 225 | 5700 | male | 2008 |
| Gentoo | Biscoe | 45.3 | 13.7 | 210 | 4300 | female | 2008 |
| Gentoo | Biscoe | 49.6 | 15.0 | 216 | 4750 | male | 2008 |
| Gentoo | Biscoe | 50.5 | 15.9 | 222 | 5550 | male | 2008 |
| Gentoo | Biscoe | 43.6 | 13.9 | 217 | 4900 | female | 2008 |
| Gentoo | Biscoe | 45.5 | 13.9 | 210 | 4200 | female | 2008 |
| Gentoo | Biscoe | 50.5 | 15.9 | 225 | 5400 | male | 2008 |
| Gentoo | Biscoe | 44.9 | 13.3 | 213 | 5100 | female | 2008 |
| Gentoo | Biscoe | 45.2 | 15.8 | 215 | 5300 | male | 2008 |
| Gentoo | Biscoe | 46.6 | 14.2 | 210 | 4850 | female | 2008 |
| Gentoo | Biscoe | 48.5 | 14.1 | 220 | 5300 | male | 2008 |
| Gentoo | Biscoe | 45.1 | 14.4 | 210 | 4400 | female | 2008 |
| Gentoo | Biscoe | 50.1 | 15.0 | 225 | 5000 | male | 2008 |
| Gentoo | Biscoe | 46.5 | 14.4 | 217 | 4900 | female | 2008 |
| Gentoo | Biscoe | 45.0 | 15.4 | 220 | 5050 | male | 2008 |
| Gentoo | Biscoe | 43.8 | 13.9 | 208 | 4300 | female | 2008 |
| Gentoo | Biscoe | 45.5 | 15.0 | 220 | 5000 | male | 2008 |
| Gentoo | Biscoe | 43.2 | 14.5 | 208 | 4450 | female | 2008 |
| Gentoo | Biscoe | 50.4 | 15.3 | 224 | 5550 | male | 2008 |
| Gentoo | Biscoe | 45.3 | 13.8 | 208 | 4200 | female | 2008 |
| Gentoo | Biscoe | 46.2 | 14.9 | 221 | 5300 | male | 2008 |
| Gentoo | Biscoe | 45.7 | 13.9 | 214 | 4400 | female | 2008 |
| Gentoo | Biscoe | 54.3 | 15.7 | 231 | 5650 | male | 2008 |
| Gentoo | Biscoe | 45.8 | 14.2 | 219 | 4700 | female | 2008 |
| Gentoo | Biscoe | 49.8 | 16.8 | 230 | 5700 | male | 2008 |
| Gentoo | Biscoe | 46.2 | 14.4 | 214 | 4650 | NA | 2008 |
| Gentoo | Biscoe | 49.5 | 16.2 | 229 | 5800 | male | 2008 |
| Gentoo | Biscoe | 43.5 | 14.2 | 220 | 4700 | female | 2008 |
| Gentoo | Biscoe | 50.7 | 15.0 | 223 | 5550 | male | 2008 |
| Gentoo | Biscoe | 47.7 | 15.0 | 216 | 4750 | female | 2008 |
| Gentoo | Biscoe | 46.4 | 15.6 | 221 | 5000 | male | 2008 |
| Gentoo | Biscoe | 48.2 | 15.6 | 221 | 5100 | male | 2008 |
| Gentoo | Biscoe | 46.5 | 14.8 | 217 | 5200 | female | 2008 |
| Gentoo | Biscoe | 46.4 | 15.0 | 216 | 4700 | female | 2008 |
| Gentoo | Biscoe | 48.6 | 16.0 | 230 | 5800 | male | 2008 |
| Gentoo | Biscoe | 47.5 | 14.2 | 209 | 4600 | female | 2008 |
| Gentoo | Biscoe | 51.1 | 16.3 | 220 | 6000 | male | 2008 |
| Gentoo | Biscoe | 45.2 | 13.8 | 215 | 4750 | female | 2008 |
| Gentoo | Biscoe | 45.2 | 16.4 | 223 | 5950 | male | 2008 |
| Gentoo | Biscoe | 49.1 | 14.5 | 212 | 4625 | female | 2009 |
| Gentoo | Biscoe | 52.5 | 15.6 | 221 | 5450 | male | 2009 |
| Gentoo | Biscoe | 47.4 | 14.6 | 212 | 4725 | female | 2009 |
| Gentoo | Biscoe | 50.0 | 15.9 | 224 | 5350 | male | 2009 |
| Gentoo | Biscoe | 44.9 | 13.8 | 212 | 4750 | female | 2009 |
| Gentoo | Biscoe | 50.8 | 17.3 | 228 | 5600 | male | 2009 |
| Gentoo | Biscoe | 43.4 | 14.4 | 218 | 4600 | female | 2009 |
| Gentoo | Biscoe | 51.3 | 14.2 | 218 | 5300 | male | 2009 |
| Gentoo | Biscoe | 47.5 | 14.0 | 212 | 4875 | female | 2009 |
| Gentoo | Biscoe | 52.1 | 17.0 | 230 | 5550 | male | 2009 |
| Gentoo | Biscoe | 47.5 | 15.0 | 218 | 4950 | female | 2009 |
| Gentoo | Biscoe | 52.2 | 17.1 | 228 | 5400 | male | 2009 |
| Gentoo | Biscoe | 45.5 | 14.5 | 212 | 4750 | female | 2009 |
| Gentoo | Biscoe | 49.5 | 16.1 | 224 | 5650 | male | 2009 |
| Gentoo | Biscoe | 44.5 | 14.7 | 214 | 4850 | female | 2009 |
| Gentoo | Biscoe | 50.8 | 15.7 | 226 | 5200 | male | 2009 |
| Gentoo | Biscoe | 49.4 | 15.8 | 216 | 4925 | male | 2009 |
| Gentoo | Biscoe | 46.9 | 14.6 | 222 | 4875 | female | 2009 |
| Gentoo | Biscoe | 48.4 | 14.4 | 203 | 4625 | female | 2009 |
| Gentoo | Biscoe | 51.1 | 16.5 | 225 | 5250 | male | 2009 |
| Gentoo | Biscoe | 48.5 | 15.0 | 219 | 4850 | female | 2009 |
| Gentoo | Biscoe | 55.9 | 17.0 | 228 | 5600 | male | 2009 |
| Gentoo | Biscoe | 47.2 | 15.5 | 215 | 4975 | female | 2009 |
| Gentoo | Biscoe | 49.1 | 15.0 | 228 | 5500 | male | 2009 |
| Gentoo | Biscoe | 47.3 | 13.8 | 216 | 4725 | NA | 2009 |
| Gentoo | Biscoe | 46.8 | 16.1 | 215 | 5500 | male | 2009 |
| Gentoo | Biscoe | 41.7 | 14.7 | 210 | 4700 | female | 2009 |
| Gentoo | Biscoe | 53.4 | 15.8 | 219 | 5500 | male | 2009 |
| Gentoo | Biscoe | 43.3 | 14.0 | 208 | 4575 | female | 2009 |
| Gentoo | Biscoe | 48.1 | 15.1 | 209 | 5500 | male | 2009 |
| Gentoo | Biscoe | 50.5 | 15.2 | 216 | 5000 | female | 2009 |
| Gentoo | Biscoe | 49.8 | 15.9 | 229 | 5950 | male | 2009 |
| Gentoo | Biscoe | 43.5 | 15.2 | 213 | 4650 | female | 2009 |
| Gentoo | Biscoe | 51.5 | 16.3 | 230 | 5500 | male | 2009 |
| Gentoo | Biscoe | 46.2 | 14.1 | 217 | 4375 | female | 2009 |
| Gentoo | Biscoe | 55.1 | 16.0 | 230 | 5850 | male | 2009 |
| Gentoo | Biscoe | 44.5 | 15.7 | 217 | 4875 | NA | 2009 |
| Gentoo | Biscoe | 48.8 | 16.2 | 222 | 6000 | male | 2009 |
| Gentoo | Biscoe | 47.2 | 13.7 | 214 | 4925 | female | 2009 |
| Gentoo | Biscoe | NA | NA | NA | NA | NA | 2009 |
| Gentoo | Biscoe | 46.8 | 14.3 | 215 | 4850 | female | 2009 |
| Gentoo | Biscoe | 50.4 | 15.7 | 222 | 5750 | male | 2009 |
| Gentoo | Biscoe | 45.2 | 14.8 | 212 | 5200 | female | 2009 |
| Gentoo | Biscoe | 49.9 | 16.1 | 213 | 5400 | male | 2009 |
| Chinstrap | Dream | 46.5 | 17.9 | 192 | 3500 | female | 2007 |
| Chinstrap | Dream | 50.0 | 19.5 | 196 | 3900 | male | 2007 |
| Chinstrap | Dream | 51.3 | 19.2 | 193 | 3650 | male | 2007 |
| Chinstrap | Dream | 45.4 | 18.7 | 188 | 3525 | female | 2007 |
| Chinstrap | Dream | 52.7 | 19.8 | 197 | 3725 | male | 2007 |
| Chinstrap | Dream | 45.2 | 17.8 | 198 | 3950 | female | 2007 |
| Chinstrap | Dream | 46.1 | 18.2 | 178 | 3250 | female | 2007 |
| Chinstrap | Dream | 51.3 | 18.2 | 197 | 3750 | male | 2007 |
| Chinstrap | Dream | 46.0 | 18.9 | 195 | 4150 | female | 2007 |
| Chinstrap | Dream | 51.3 | 19.9 | 198 | 3700 | male | 2007 |
| Chinstrap | Dream | 46.6 | 17.8 | 193 | 3800 | female | 2007 |
| Chinstrap | Dream | 51.7 | 20.3 | 194 | 3775 | male | 2007 |
| Chinstrap | Dream | 47.0 | 17.3 | 185 | 3700 | female | 2007 |
| Chinstrap | Dream | 52.0 | 18.1 | 201 | 4050 | male | 2007 |
| Chinstrap | Dream | 45.9 | 17.1 | 190 | 3575 | female | 2007 |
| Chinstrap | Dream | 50.5 | 19.6 | 201 | 4050 | male | 2007 |
| Chinstrap | Dream | 50.3 | 20.0 | 197 | 3300 | male | 2007 |
| Chinstrap | Dream | 58.0 | 17.8 | 181 | 3700 | female | 2007 |
| Chinstrap | Dream | 46.4 | 18.6 | 190 | 3450 | female | 2007 |
| Chinstrap | Dream | 49.2 | 18.2 | 195 | 4400 | male | 2007 |
| Chinstrap | Dream | 42.4 | 17.3 | 181 | 3600 | female | 2007 |
| Chinstrap | Dream | 48.5 | 17.5 | 191 | 3400 | male | 2007 |
| Chinstrap | Dream | 43.2 | 16.6 | 187 | 2900 | female | 2007 |
| Chinstrap | Dream | 50.6 | 19.4 | 193 | 3800 | male | 2007 |
| Chinstrap | Dream | 46.7 | 17.9 | 195 | 3300 | female | 2007 |
| Chinstrap | Dream | 52.0 | 19.0 | 197 | 4150 | male | 2007 |
| Chinstrap | Dream | 50.5 | 18.4 | 200 | 3400 | female | 2008 |
| Chinstrap | Dream | 49.5 | 19.0 | 200 | 3800 | male | 2008 |
| Chinstrap | Dream | 46.4 | 17.8 | 191 | 3700 | female | 2008 |
| Chinstrap | Dream | 52.8 | 20.0 | 205 | 4550 | male | 2008 |
| Chinstrap | Dream | 40.9 | 16.6 | 187 | 3200 | female | 2008 |
| Chinstrap | Dream | 54.2 | 20.8 | 201 | 4300 | male | 2008 |
| Chinstrap | Dream | 42.5 | 16.7 | 187 | 3350 | female | 2008 |
| Chinstrap | Dream | 51.0 | 18.8 | 203 | 4100 | male | 2008 |
| Chinstrap | Dream | 49.7 | 18.6 | 195 | 3600 | male | 2008 |
| Chinstrap | Dream | 47.5 | 16.8 | 199 | 3900 | female | 2008 |
| Chinstrap | Dream | 47.6 | 18.3 | 195 | 3850 | female | 2008 |
| Chinstrap | Dream | 52.0 | 20.7 | 210 | 4800 | male | 2008 |
| Chinstrap | Dream | 46.9 | 16.6 | 192 | 2700 | female | 2008 |
| Chinstrap | Dream | 53.5 | 19.9 | 205 | 4500 | male | 2008 |
| Chinstrap | Dream | 49.0 | 19.5 | 210 | 3950 | male | 2008 |
| Chinstrap | Dream | 46.2 | 17.5 | 187 | 3650 | female | 2008 |
| Chinstrap | Dream | 50.9 | 19.1 | 196 | 3550 | male | 2008 |
| Chinstrap | Dream | 45.5 | 17.0 | 196 | 3500 | female | 2008 |
| Chinstrap | Dream | 50.9 | 17.9 | 196 | 3675 | female | 2009 |
| Chinstrap | Dream | 50.8 | 18.5 | 201 | 4450 | male | 2009 |
| Chinstrap | Dream | 50.1 | 17.9 | 190 | 3400 | female | 2009 |
| Chinstrap | Dream | 49.0 | 19.6 | 212 | 4300 | male | 2009 |
| Chinstrap | Dream | 51.5 | 18.7 | 187 | 3250 | male | 2009 |
| Chinstrap | Dream | 49.8 | 17.3 | 198 | 3675 | female | 2009 |
| Chinstrap | Dream | 48.1 | 16.4 | 199 | 3325 | female | 2009 |
| Chinstrap | Dream | 51.4 | 19.0 | 201 | 3950 | male | 2009 |
| Chinstrap | Dream | 45.7 | 17.3 | 193 | 3600 | female | 2009 |
| Chinstrap | Dream | 50.7 | 19.7 | 203 | 4050 | male | 2009 |
| Chinstrap | Dream | 42.5 | 17.3 | 187 | 3350 | female | 2009 |
| Chinstrap | Dream | 52.2 | 18.8 | 197 | 3450 | male | 2009 |
| Chinstrap | Dream | 45.2 | 16.6 | 191 | 3250 | female | 2009 |
| Chinstrap | Dream | 49.3 | 19.9 | 203 | 4050 | male | 2009 |
| Chinstrap | Dream | 50.2 | 18.8 | 202 | 3800 | male | 2009 |
| Chinstrap | Dream | 45.6 | 19.4 | 194 | 3525 | female | 2009 |
| Chinstrap | Dream | 51.9 | 19.5 | 206 | 3950 | male | 2009 |
| Chinstrap | Dream | 46.8 | 16.5 | 189 | 3650 | female | 2009 |
| Chinstrap | Dream | 45.7 | 17.0 | 195 | 3650 | female | 2009 |
| Chinstrap | Dream | 55.8 | 19.8 | 207 | 4000 | male | 2009 |
| Chinstrap | Dream | 43.5 | 18.1 | 202 | 3400 | female | 2009 |
| Chinstrap | Dream | 49.6 | 18.2 | 193 | 3775 | male | 2009 |
| Chinstrap | Dream | 50.8 | 19.0 | 210 | 4100 | male | 2009 |
| Chinstrap | Dream | 50.2 | 18.7 | 198 | 3775 | female | 2009 |
\[ \begin{equation} y_i = \beta_0 + \beta_1x_i + \epsilon_i, \qquad i=1,...,n \end{equation} \] where \(y\) is the dependent variable, \(x\) is the independent variable (also called explanatory variable), \(\beta_0\) is the intercept parameter, \(\beta_1\) is the slope parameter and \(\epsilon\sim N(0,\sigma^2)\) is the error coefficient.
set.seed(123) x <- seq(0,5,0.1) y <- x + rnorm(length(x)) plot(x, y)
mod <- lm(y~x) summary(mod)
## ## Call: ## lm(formula = y ~ x) ## ## Residuals: ## Min 1Q Median 3Q Max ## -2.0116 -0.6110 -0.0912 0.6575 2.1444 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 0.05829 0.25565 0.228 0.821 ## x 0.99216 0.08812 11.259 3.4e-15 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 0.9263 on 49 degrees of freedom ## Multiple R-squared: 0.7212, Adjusted R-squared: 0.7155 ## F-statistic: 126.8 on 1 and 49 DF, p-value: 3.397e-15
plot(x,y) abline(reg = mod)
Our hypothesis is that there is no relationship between the variables. The p-value expresses the probability of this hypothesis. If our p-value is very small, it is very unlikely, that this hypothesis is true and we have a statistical significant relationship between the variables.
plot(mod, which=1)
plot(mod, which=2)
2nd graph: Normality of errors
Normal QQ plot = plot of the ordered residuals against the fitted values
Should be a reasonably straight line
plot(mod, which=3)
plot(mod, which=4)
plot(x,y)
res <- residuals(mod) plot(x, res)
H0: There is no correlation among the residuals.
HA: The residuals are autocorrelated.
library(car)
## Loading required package: carData
durbinWatsonTest(mod)
## lag Autocorrelation D-W Statistic p-value ## 1 0.02431999 1.940949 0.712 ## Alternative hypothesis: rho != 0
plot(fitted(mod),res)
qqPlot(res)
## [1] 44 18
shapiro.test(res)
## ## Shapiro-Wilk normality test ## ## data: res ## W = 0.99152, p-value = 0.9732
If not another model might be more appropriate:
For example many zeros:
You could separate in two analyses: Binomial model for success / failure and linear model for successes
4th graph of plot(model): Plot of Cook´s distances versus raw data → Highlights the identity of particularly influential data points
Polynomial regression e.g. a quadratic function might be an option
lm(y ~ x + I(x^2))
You could for example use a log transformation of the response variable
lm(log(y) ~ x)
\[\begin{equation}\label{eqn:linearregression} y_i = \beta_0 + \beta_1x_{1,i} + ... + \beta_px_{p,i} + \epsilon_i, \qquad i=1,...,n \end{equation}\] where \(y\) is the dependent variable, \(x_1 ... x_p\) are the independent variables (also called explanatory variables), \(\beta_0 ... \beta_p\) are the regression coefficients, \(\epsilon\sim N(0,\sigma^2)\) is the error coefficient and \(p \geq 1\).
df.4 <- df[df$hive==4,] # plot temperature outside plot(df.4$time, df.4$t_i_3)
# plot temperature outside
plot(df.4$time, df.4$t_i_3,
ylim=c(0,40)) # added this
# plot temperature outside
plot(df.4$time, df.4$t_i_3,
ylim = c(0,40),
xlab = "Time (2019)", # added this
ylab = "Temperature within hive", # added this
main = "Sensor measurements") # added this
# plot temperature outside
plot(df.4$time, df.4$t_i_3,
ylim = c(0,40),
type = "b", # added this
lty = 1, # added this
xlab = "Time (2019)",
ylab = "Temperature within hive",
main = "Sensor measurements")
Source: http://www.sthda.com/english/wiki/line-types-in-r-lty
df.4 <- df.4[df.4$t_i_3>5&df.4$t_i_3<40,] # added this
# plot temperature outside
plot(df.4$time, df.4$t_i_3,
ylim = c(0,40),
type = "b",
lty = 1,
xlab = "Time (2019)",
ylab = "Temperature within hive",
main = "Sensor measurements")
# plot temperature outside
plot(df.4$time, df.4$t_i_3,
ylim = c(0,40),
type = "b",
lty = 1,
pch = 4, # added this
xlab = "Time (2019)",
ylab = "Temperature within hive",
main = "Sensor measurements")
Source: http://www.sthda.com/english/wiki/r-plot-pch-symbols-the-different-point-shapes-available-in-r
# plot temperature outside
plot(df.4$time, df.4$t_i_3,
ylim = c(0,40),
type = "b",
lty = 1,
pch = 4,
xlim = as.POSIXct(c("2019-08-08", "2019-08-09")), # added this
xlab = "Time (2019-08-08)",
ylab = "Temperature within hive",
main = "Sensor measurements")
# plot temperature outside
plot(df.4$time, df.4$t_i_3,
ylim = c(0,40),
type = "b",
lty = 1,
pch = 4,
xlim = as.POSIXct(c("2019-08-08", "2019-08-09")),
xlab = "Time (2019-08-08)",
ylab = "Temperature within hive",
main = "Sensor measurements",
xaxt="n")
axis.POSIXct(1,
at=seq(min(df.4$time), max(df.4$time), by="1 hour"),
format="%H:00") # added this
# subset data
df.4 <- df[df$hive==4,]
# plot temperature outside
plot(df.4$time, df.4$t_o, ylim=c(0,40),type = 'p', pch=4)
# choose colours
cl <- rainbow(5)
# choose colums
cols <- 4:8
# plot each column
for (i in 1:5){
lines(df.4$time,
df.4[,cols[i]],
col = cl[i],
type = 'p',
pch=4,
ylim=c(0,40))
}
# add legend
legend("topright", legend=c(1, 2, 3, 4, 5, "outside"),
col=c(cl, "black"), pch = 4, lty = 0, cex=0.8)
# add legend
legend("topright", legend=c(1, 2, 3, 4, 5, "outside"),
col=c(cl, "black"), pch = 4, lty = 0, cex=0.8)
# plot data library(ggplot2) ggplot(data = df.4, aes(x=time, y=t_i_3)) + geom_point()
# plot data
library(ggplot2)
ggplot(data = df.4, aes(x=time, y=t_i_3)) + geom_point(shape=4) +
ylim(c(0, 40)) +
xlab("Time (2019") +
ylab("Temperature within hive") +
ggtitle("Sensor measurements")
## Warning: Removed 152 rows containing missing values or values outside the scale range ## (`geom_point()`).
# subset data
df.4 <- df[df$hive==4,]
# choose columns
df.4.cols <- df.4[,c(1,4:9)]
# reshape data
library(reshape)
mdf <- melt(df.4.cols, id=c("time"))
# plot data
library(ggplot2)
ggplot(data = mdf, aes(x=time, y=value)) +
geom_line(aes(colour=variable)) +
ylim(c(0, 40))
library(plotly) fig <- plot_ly(df.4[1:100,], x = ~time, y = ~t_i_3) fig
p <- plot_ly(df.4[1:100,], x = ~time, y = ~t_i_3) # saving the plot as html htmlwidgets::saveWidget(p, "testploty.html")
## No trace type specified: ## Based on info supplied, a 'scatter' trace seems appropriate. ## Read more about this trace type -> https://plotly.com/r/reference/#scatter
## No scatter mode specifed: ## Setting the mode to markers ## Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode
# if you want to have a static image, e.g. png, you can then open the html file, and use the small button with the camera symbol to export as png. You could also use the library orca to directly export a static image using plotly directly from your code, but it seems to be a bit complicated to install orca. # I haven't used powerpoint for a long time, but I think there should be a way to embed a html file: https://www.techwalla.com/articles/how-to-embed-an-excel-workbook-icon-into-powerpoint
png("test.png")
plot(hist(rnorm(100)))
dev.off()
If you want to do an assignment, please send me an e-mail:
“I want to do an assignment.””
“I do not want a grade on my certificate. / I want to have a grade on my certificate./ I want to have a grade if it is better than <1.3, 1.7, 2.0, 2.3, 2.7, 3.0, …>”